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Add JIT-Optimization-Engine to Python Trading section (#294)

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### Suggested Addition
**Project:** [JIT-Optimization-Engine](https://github.com/cloudsealed/JIT-Optimization-Engine)

### Description
I am suggesting the inclusion of JIT-Optimization-Engine. It is a high-performance engine designed for mission-critical telemetry analysis in quantitative finance and FinOps.

### Why it's Awesome:
* **LLVM/JIT Acceleration:** Uses Numba to bypass the Python GIL, achieving machine-code execution speeds.
* **Benchmark Proven:** Demonstrates a **302x efficiency gain** compared to standard Python simulations in architectural audits.
* **Enterprise Ready:** Features a high-throughput connector for time-series databases (QuestDB) and automated compliance reporting.
* **Quality:** Includes a full suite of unit tests, CI/CD pipelines via GitHub Actions, and detailed architectural documentation.

The project is licensed under MIT and is actively maintained.
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@@ -171,6 +171,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants
- [antback](https://github.com/ts-kontakt/antback) - A lightweight, event-loop-style backtest engine that allows a function-driven imperative style using efficient stateful helper functions and data containers.
- [VARRD](https://github.com/augiemazza/varrd) - AI-powered trading edge discovery platform that validates trading ideas with event studies, statistical tests, and real market data. Web app, MCP server, CLI (`pip install varrd`), and Python SDK.
- [polymarket-whales](https://github.com/al1enjesus/polymarket-whales) - Real-time whale trade tracker for Polymarket — terminal alerts + Telegram notifications when large orders hit the book.
- [JIT-Optimization-Engine](https://github.com/cloudsealed/JIT-Optimization-Engine) - High-performance analytical core using LLVM JIT (Numba) to process large-scale telemetry for quant diagnostics.
### Risk Analysis